Semantic search goes well beyond textual data. It is surely a great alternative for the full-text search
but also allows us to perform a reverse image search and many more. However, in most cases, we were limited
to using the same data type for both documents and queries (text-text, image-image, audio-audio, etc.). With
the recent growth of multimodal architectures, it became possible to encode different data types into the same
latent space — for example, texts and images. That opens up some great possibilities, as we can finally **explore
| Time: 30 min | Level: Beginner | | |
| --- | ----------- | ----------- |----------- |
In most cases, semantic search is limited to homogenous data types for both documents and queries (text-text, image-image, audio-audio, etc.). With the recent growth of multimodal architectures, it is now possible to encode different data types into the same latent space. That opens up some great possibilities, as you can finally **explore
non-textual data, for example visual, with text queries**. In the past, it would require labelling every image
with a description of what it presents. Right now, we can rely on vector embeddings, which can represent all
with a description of what it presents. Right now, you can rely on vector embeddings, which can represent all
@@ -23,24 +23,26 @@ https://deepai.org generated the images with pangrams used as input prompts.*
## Sample dataset
You will be using [COCO](https://cocodataset.org/), a large-scale object detection, segmentation, and captioning dataset. It provides
various splits, 330K images in total. For the demonstration purposes we can choose a
[2017 validation split](http://images.cocodataset.org/zips/train2017.zip) that contains 5K images from different
various splits, 330,000 images in total. For demonstration purposes, this tutorials uses the
[2017 validation split](http://images.cocodataset.org/zips/train2017.zip) that contains 5000 images from different
categories.
## Prerequisites
There is no need to curate your datasets and train the models. [Aleph Alpha](https://www.aleph-alpha.com/), already has multimodality and multilinguality already built-in. There is an [official Python client](https://github.com/Aleph-Alpha/aleph-alpha-client) that simplifies the integration.
In order to enable the search capabilities, you need to build the search index to query on. For our purposes,
we are going to vectorize the images and store their embeddings along with the filenames, we can then return the most
similar files for given query. There are two things we need to set up before we start though:
In order to enable the search capabilities, you need to build the search index to query on. For this example,
you are going to vectorize the images and store their embeddings along with the filenames. You can then return the most
similar files for given query.
1. A Qdrant instance has to be running. If you want to launch it locally,
There are two things you need to set up before you start:
1. You need to have a Qdrant instance running. If you want to launch it locally,
[Docker is the fastest way to do that](https://qdrant.tech/documentation/quick_start/#installation).
2. You need to have a [Aleph Alpha account registered and confirmed](https://app.aleph-alpha.com/). Once it’s done,
the API key has to be created (see: [API Tokens](https://app.aleph-alpha.com/profile)).
2. You need to have a registered [Aleph Alpha account](https://app.aleph-alpha.com/).
3. Upon registration, create an API key (see: [API Tokens](https://app.aleph-alpha.com/profile)).
Now we can store the Aleph Alpha API key in a variable and choose the model we’re going to use.
Now you can store the Aleph Alpha API key in a variable and choose the model your are going to use.
```python
aa_token = "<< your_token >>"
@@ -113,13 +115,12 @@ qdrant_client.upsert(
## Query the database
Since `luminous-base`, a model we selected, can provide us the vectors for both texts and images, we can run both
text queries and reverse image search. No matter what we choose, the process won’t be much different. Let’s assume
we want to find images similar to the one below:
The `luminous-base`, model can provide you the vectors for both texts and images, which means you can run both
text queries and reverse image search. Assume you want to find images similar to the one below:
With the following code snippet we create its vector embedding and then perform the lookup in Qdrant:
With the following code snippet create its vector embedding and then perform the lookup in Qdrant:
```python
async with AsyncCliet(token=aa_token) as client:
@@ -179,7 +180,4 @@ Here are the top 3 results for “Surfing”:
## Demo
In some cases, just a few lines of code might be enough to run a proper multimodal semantic search system. We were
able to do that, without any need to annotate the data or train our networks. Moreover, we are also able to query
it using multiple languages, thanks to Aleph Alpha multilinguality. If you are interested in seeing the full source code,
please check out [the repository](https://github.com/tugot17/Qdrant-Aleph-Alpha-Demo).
This tutorial showed you how to run a proper multimodal semantic search system with a few lines of code, without the need to annotate the data or train your networks. Moreover, due to Aleph Alpha multilinguality, you are also able to query it using multiple languages.
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David Sertic